Noise is an unavoidable signal component in vibration testing of practical engineering structures. When true structural modes are submerged in noise, the traditional de-noising method may eliminate noise and parts of true modes to cause structural natural vibration information loss. Here, a new de-noising method suitable for test signals with high background noise was proposed. With this method, a measured signal was regarded as a linear superposition of a series of complex exponential signal components, and they were represented as a series of extreme values and residues based on a lower order state space model. The conversion relations between complex exponential components’ extreme values and frequencies were established. Imposing a frequency window, extreme values and residue vectors in a predetermined interval were separated to acquire a reconstructed signal after de-noising. It was shown that compared with the traditional higher order model, adopting a lower order state space model can reduce significantly a matrix’s conditioning number, and obtain a better numerical stability; representing a measured signal as a series of complex exponential signal components can overcome the intrinsic resolution problem of Fourier decomposition technique and have a wider generality. A mass-spring-damper model was firstly adopted, and test signals with different signal-noise ratios were constructed to study the new method’s de-noising effect. Results showed that when the SNRs of test signals are 40 dB, 30 dB, 20 dB and 10 dB, respectively, the new approach can effectively eliminate noise. To further verify the effectiveness of the proposed method, acceleration response signals of an actual offshore platform were collected. The results showed that after de-noising with the new method, the measured signals’ frequency components agree well with those of existing recorded data in 1994.
Key words
pole-residue decomposition /
signal noise elimination /
Fourier transformation /
signal reconstruction /
modal identification
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